23 research outputs found

    Determinação e avaliação de indicadores espaço-temporais da qualidade de dados no mapeamento colaborativo

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    Orientador: Prof. Dr. Marcio Augusto Reolon SchmidtCo-Orientadora: Profª. Drª. Silvana Philippi CamboimTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências da Terra, Programa de Pós-Graduação em Ciências Geodésicas. Defesa : Curitiba, 28/11/2022Inclui referênciasResumo: Questões relacionadas com a desatualização do mapeamento oficial são recorrentes em diferentes localidades do Brasil e do mundo, principalmente devido ao custo-benefício para a sua produção e manutenção. Neste contexto, pesquisas têm direcionado esforços em avaliar o potencial de informações oriundas de plataformas de mapeamento colaborativo, no âmbito de estabelecer o seu potencial de integração e a determinação da qualidade. Abordagens tradicionais se baseiam em comparações em relação a bases oficiais existentes, todavia, esta nem sempre é a realidade para as cidades brasileiras, uma vez que, o mapeamento oficial pode ser inexistente ou desatualizado. Este aspecto tem impulsionado pesquisas a relacionar a qualidade dos dados colaborativos em relação a seus parâmetros intrínsecos, caracterizados por históricos de edições, quantidade de contribuições e contribuidores. Com base em tais questões, questiona-se nesta pesquisa se é possível modelar os padrões espaço-temporais de qualidade intrínseca que influenciam na completude dos dados da plataforma OpenStreetMap (OSM) para obter a sua qualidade. Além disso, questiona-se também se é possível desenvolver ferramentas para a avaliação da qualidade extrínseca na qual seja possível identificar e discutir questões acerca da heterogeneidade dos dados. Dessa forma, este trabalho objetivou o desenvolvimento de uma metodologia para modelar e avaliar padrões espaço-temporais dos indicadores de qualidade intrínseca dos dados plataforma OSM e sua relação com os indicadores de qualidade tradicionais. Foi desenvolvido um procedimento metodológico para modelar as contribuições ao longo do tempo e, a partir dos parâmetros obtidos, identificar de que maneira os padrões se comportam e quais os fatores que influenciam na sua heterogeneidade. Foram desenvolvidos complementos para avaliar e visualizar a acurácia posicional e completude no OSM, de modo que auxiliem na tomada de decisões. A modelagem matemática deu-se a partir da Regressão Logística, em células de 1x1 km. Como resultados, notou-se que o parâmetro de inclinação da curva permitiu diferenciar regiões com grandes contribuições e de crescimento gradativo ao longo do tempo, e até mesmo, a sinergia entre o OSM e os dados oficiais, a partir da importação de feições. Além disso, notou-se que existe uma relação direta da completude dos dados e a saturação da curva (estabilidade das contribuições nos últimos anos), principalmente nas análises que envolvem os eixos viários. Conclui-se que é possível utilizar os padrões espaço-temporais de contribuição como medida de qualidade intrínseca, diante das questões relacionadas com a qualidade dos dados e recomenda-se a continuidade das análises, utilizando diferentes regiões de estudo, categoriais e tamanhos de células.Abstract: Issues related to the outdated status of official mapping are recurrent in different locations in Brazil and around the world, especially due to the cost-effectiveness of its production and maintenance. In this context, research is focusing its efforts on evaluating the potential of information coming from collaborative mapping, in the context of establishing their integrating and determining quality potential. Traditional approaches are based on comparisons with existing official databases, however, this is not always the reality for Brazilian cities, since the official mapping may be non-existent or outdated. This aspect has driven research to relate the quality of collaborative data in relation to its intrinsic parameters, characterized by historical editions, number of contributions and contributors. Based on these questions, this research asks whether it is possible to model the spatiotemporal patterns of intrinsic quality that influence the completeness of the OpenStreetMap (OSM) platform data to obtain its quality. In addition, it is also questioned whether it is possible to develop tools to evaluate the extrinsic quality in which it is possible to identify and discuss questions over the heterogeneity of the data. Thus, this research aimed to develop a methodology to model and evaluate spatiotemporal patterns of the intrinsic quality indicators of data from OSM and its relationship with the traditional quality indicators. A methodological procedure was developed to model the contributions over time and based on the obtained parameters, identify how the patterns behave and which factors influence their heterogeneity. Complements were developed to evaluate and visualize the OSM positional accuracy and completeness, in order to help in decision making. The Logistic Regression based the mathematic modeling, in cells of 1x1 km. As a result, it was possible to notice that the curve slope parameter allowed to differentiate regions with large contributions and gradual growth over time, and even the synergy between OSM and official data, from the import of features. Besides that, it was noticed that there is a direct relationship between the completeness of data and the curve saturation (stability of the contributions in the last years), especially in the analysis involving road axles. It is concluded that it is possible to use spatiotemporal patterns of contributions as a measure of intrinsic quality, regarding the issues related to data quality. It is recommended to continue the analysis using different study regions, categories and cell sizes

    Advanced Location-Based Technologies and Services

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    Since the publication of the first edition in 2004, advances in mobile devices, positioning sensors, WiFi fingerprinting, and wireless communications, among others, have paved the way for developing new and advanced location-based services (LBSs). This second edition provides up-to-date information on LBSs, including WiFi fingerprinting, mobile computing, geospatial clouds, geospatial data mining, location privacy, and location-based social networking. It also includes new chapters on application areas such as LBSs for public health, indoor navigation, and advertising. In addition, the chapter on remote sensing has been revised to address advancements

    Geoinformatics in Citizen Science

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    The book features contributions that report original research in the theoretical, technological, and social aspects of geoinformation methods, as applied to supporting citizen science. Specifically, the book focuses on the technological aspects of the field and their application toward the recruitment of volunteers and the collection, management, and analysis of geotagged information to support volunteer involvement in scientific projects. Internationally renowned research groups share research in three areas: First, the key methods of geoinformatics within citizen science initiatives to support scientists in discovering new knowledge in specific application domains or in performing relevant activities, such as reliable geodata filtering, management, analysis, synthesis, sharing, and visualization; second, the critical aspects of citizen science initiatives that call for emerging or novel approaches of geoinformatics to acquire and handle geoinformation; and third, novel geoinformatics research that could serve in support of citizen science

    Remote Sensing for Land Administration

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    LIPIcs, Volume 277, GIScience 2023, Complete Volume

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    LIPIcs, Volume 277, GIScience 2023, Complete Volum

    Spatiotemporal enabled Content-based Image Retrieval

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    Big Data Computing for Geospatial Applications

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    The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms

    Urban Informatics

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    This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity
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